Apache Airflow is extremely powerful for orchestrating complex workflows and scheduling tasks across various systems. Its DAG-based approach offers excellent visibility and control over dependencies. The wide range of integrations and plugins makes it highly adaptable, while the active open-source community ensures continuous improvements and resources for troubleshooting. Review collected by and hosted on G2.com.

The initial setup and configuration can be complex, especially for newcomers. The learning curve is steep, and performance tuning for large-scale workflows often requires significant expertise. The UI, while functional, can sometimes feel outdated and less intuitive compared to modern workflow tools. Additionally, upgrading to newer versions may require careful migration planning to avoid breaking changes. Review collected by and hosted on G2.com.

Apache Airflow makes managing complicated data tasks much easier being able to code workflows in Python is great, and the UI helps me spot issues fast. Love how it connects with everything and the built-in tools save time. Review collected by and hosted on G2.com.

Advanced setup (scaling, security) can be tricky

The UI gets cluttered with too many workflows Review collected by and hosted on G2.com.

Scheduling daily/weekly/monthly data reports. Review collected by and hosted on G2.com.

Can be demanding on CPU/memory for large deployments Review collected by and hosted on G2.com.

The best thing about Apache Airflow is its flexibility and scalability in orchestrating complex workflows. Review collected by and hosted on G2.com.

A common dislike about Apache Airflow is that it has a steep learning curve and requires significant setup and maintenance for production use. It takes time to master it Review collected by and hosted on G2.com.

Platforms like AWS and GCP charge for the ETL workflows where as Apache Airflow is easy to use and host on standalone server or even on Kubernetes Review collected by and hosted on G2.com.

When hosted on kubernetes it slows down as kubernetes pod schedulers are not meant for memory intensic ops Review collected by and hosted on G2.com.

We use Apache airflow to process realtime data in our data lake and to run batch jobs in a workflow Review collected by and hosted on G2.com.

At times it becomes little tricky to run ETL for realtime data apps Review collected by and hosted on G2.com.

The ability to schedule multiple automated flows from different tools like nessus, open vas and to get the results at one place Review collected by and hosted on G2.com.

UI could be better. With detailed guide missing Review collected by and hosted on G2.com.

ability to manage dependencies, handle retries, and provide detailed insights into workflow execution through robust monitoring and logging capabilities. Review collected by and hosted on G2.com.

Difficult to install in windows operating Review collected by and hosted on G2.com.

The main best of airflow is it's a friendly to understand and easy to work on even with basic knowledge Review collected by and hosted on G2.com.

Not much is there, the changes are take time to reflect in the airflow Review collected by and hosted on G2.com.

Lot of features to use and very helpful for processing batch data. Review collected by and hosted on G2.com.

There is learning curve, python and DAG knowledge is required. Review collected by and hosted on G2.com.